{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## 取得数据，并引入基本函数库"
      ],
      "metadata": {
        "id": "Qk7zsCyc_B7s"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1EhlneeR-vLe",
        "outputId": "78673e66-12a5-4575-a554-401ef6ca2b94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-11-16 08:00:05--  http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat [following]\n",
            "--2024-11-16 08:00:05--  https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5953527 (5.7M)\n",
            "Saving to: ‘Indian_pines_corrected.mat’\n",
            "\n",
            "Indian_pines_correc 100%[===================>]   5.68M  2.03MB/s    in 2.8s    \n",
            "\n",
            "2024-11-16 08:00:09 (2.03 MB/s) - ‘Indian_pines_corrected.mat’ saved [5953527/5953527]\n",
            "\n",
            "URL transformed to HTTPS due to an HSTS policy\n",
            "--2024-11-16 08:00:09--  https://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1125 (1.1K)\n",
            "Saving to: ‘Indian_pines_gt.mat’\n",
            "\n",
            "Indian_pines_gt.mat 100%[===================>]   1.10K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-11-16 08:00:09 (64.0 MB/s) - ‘Indian_pines_gt.mat’ saved [1125/1125]\n",
            "\n",
            "Collecting spectral\n",
            "  Downloading spectral-0.23.1-py3-none-any.whl.metadata (1.3 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from spectral) (1.26.4)\n",
            "Downloading spectral-0.23.1-py3-none-any.whl (212 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.9/212.9 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: spectral\n",
            "Successfully installed spectral-0.23.1\n"
          ]
        }
      ],
      "source": [
        "! wget http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
        "! wget http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\n",
        "! pip install spectral"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.io as sio\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score\n",
        "import spectral\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "S9M0v7mL_Ktm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 定义 HybridSN 类（有SE机制、无softmax）"
      ],
      "metadata": {
        "id": "r_qTzI9gAKDu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Indian_pines数据集有16个种类，因此定义class_num=16\n",
        "class_num = 16\n",
        "\n",
        "# 定义SE块\n",
        "class se_block(nn.Module):\n",
        "    def __init__(self, channel, ratio=16):\n",
        "        super(se_block, self).__init__()\n",
        "        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Linear(channel, channel // ratio, bias=False),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(channel // ratio, channel, bias=False),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        b, c, _, _ = x.size()\n",
        "        y = self.avg_pool(x).view(b, c)\n",
        "        y = self.fc(y).view(b, c, 1, 1)\n",
        "        return x * y\n",
        "\n",
        "# 定义HybridSN，在HybridSH网络中加入SENet注意力机制：\n",
        "class HybridSN(nn.Module):\n",
        "    def __init__(self, in_channels=1, out_channels=class_num):\n",
        "        super(HybridSN, self).__init__()\n",
        "        # 三维卷积层\n",
        "        self.conv3d = nn.Sequential(\n",
        "            nn.Conv3d(1, 8, kernel_size=(7, 3, 3), stride=1, padding=0),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv3d(8, 16, kernel_size=(5, 3, 3), stride=1, padding=0),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv3d(16, 32, kernel_size=(3, 3, 3), stride=1, padding=0),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        # 二维卷积层\n",
        "        self.conv2d = nn.Sequential(\n",
        "            nn.Conv2d(576, 64, kernel_size=(3, 3), stride=1, padding=0),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        # 全连接层\n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Linear(64 * 17 * 17, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.4),\n",
        "            nn.Linear(256, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.4),\n",
        "            nn.Linear(128, 16)\n",
        "        )\n",
        "        # 论文里有加softmax，但本次实验下loss下降特别慢，因此没有使用\n",
        "        # self.softmax = nn.Softmax(dim=1)\n",
        "        self.se = se_block(32 * 18)\n",
        "\n",
        "    # 正向传播过程\n",
        "    def forward(self, t):\n",
        "        t = self.conv3d(t)\n",
        "        # 进行卷积降维\n",
        "        t = t.view(t.shape[0], t.shape[1] * t.shape[2], t.shape[3], t.shape[4])\n",
        "        # 加入senet机制\n",
        "        t = self.se(t)\n",
        "        t = self.conv2d(t)\n",
        "        # 进行卷积降维进行flatten\n",
        "        t = t.view(t.shape[0], -1)\n",
        "        t = self.fc(t)\n",
        "        # t = self.softmax(t)\n",
        "        return t\n"
      ],
      "metadata": {
        "id": "Un9zxr_zAM1s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 读取并创建数据集"
      ],
      "metadata": {
        "id": "R_v2EpebAPLn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 对高光谱数据 X 应用 PCA 变换\n",
        "def applyPCA(X, numComponents):\n",
        "    newX = np.reshape(X, (-1, X.shape[2]))\n",
        "    pca = PCA(n_components=numComponents, whiten=True)\n",
        "    newX = pca.fit_transform(newX)\n",
        "    newX = np.reshape(newX, (X.shape[0], X.shape[1], numComponents))\n",
        "    return newX\n",
        "\n",
        "# 对单个像素周围提取 patch 时，边缘像素就无法取了，因此，给这部分像素进行 padding 操作\n",
        "def padWithZeros(X, margin=2):\n",
        "    newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))\n",
        "    x_offset = margin\n",
        "    y_offset = margin\n",
        "    newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\n",
        "    return newX\n",
        "\n",
        "# 在每个像素周围提取 patch ，然后创建成符合 keras 处理的格式\n",
        "def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):\n",
        "    # 给 X 做 padding\n",
        "    margin = int((windowSize - 1) / 2)\n",
        "    zeroPaddedX = padWithZeros(X, margin=margin)\n",
        "    # split patches\n",
        "    patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))\n",
        "    patchesLabels = np.zeros((X.shape[0] * X.shape[1]))\n",
        "    patchIndex = 0\n",
        "    for r in range(margin, zeroPaddedX.shape[0] - margin):\n",
        "        for c in range(margin, zeroPaddedX.shape[1] - margin):\n",
        "            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]\n",
        "            patchesData[patchIndex, :, :, :] = patch\n",
        "            patchesLabels[patchIndex] = y[r-margin, c-margin]\n",
        "            patchIndex = patchIndex + 1\n",
        "    if removeZeroLabels:\n",
        "        patchesData = patchesData[patchesLabels>0,:,:,:]\n",
        "        patchesLabels = patchesLabels[patchesLabels>0]\n",
        "        patchesLabels -= 1\n",
        "    return patchesData, patchesLabels\n",
        "\n",
        "def splitTrainTestSet(X, y, testRatio, randomState=345):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState, stratify=y)\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "# 地物类别\n",
        "class_num = 16\n",
        "X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']\n",
        "y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']\n",
        "\n",
        "# 用于测试样本的比例\n",
        "test_ratio = 0.90\n",
        "# 每个像素周围提取 patch 的尺寸\n",
        "patch_size = 25\n",
        "# 使用 PCA 降维，得到主成分的数量\n",
        "pca_components = 30\n",
        "\n",
        "print('Hyperspectral data shape: ', X.shape)\n",
        "print('Label shape: ', y.shape)\n",
        "\n",
        "print('\\n... ... PCA tranformation ... ...')\n",
        "X_pca = applyPCA(X, numComponents=pca_components)\n",
        "print('Data shape after PCA: ', X_pca.shape)\n",
        "\n",
        "print('\\n... ... create data cubes ... ...')\n",
        "X_pca, y = createImageCubes(X_pca, y, windowSize=patch_size)\n",
        "print('Data cube X shape: ', X_pca.shape)\n",
        "print('Data cube y shape: ', y.shape)\n",
        "\n",
        "print('\\n... ... create train & test data ... ...')\n",
        "Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X_pca, y, test_ratio)\n",
        "print('Xtrain shape: ', Xtrain.shape)\n",
        "print('Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "# 改变 Xtrain, Ytrain 的形状，以符合 keras 的要求\n",
        "Xtrain = Xtrain.reshape(-1, patch_size, patch_size, pca_components, 1)\n",
        "Xtest  = Xtest.reshape(-1, patch_size, patch_size, pca_components, 1)\n",
        "print('before transpose: Xtrain shape: ', Xtrain.shape)\n",
        "print('before transpose: Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "# 为了适应 pytorch 结构，数据要做 transpose\n",
        "Xtrain = Xtrain.transpose(0, 4, 3, 1, 2)\n",
        "Xtest  = Xtest.transpose(0, 4, 3, 1, 2)\n",
        "print('after transpose: Xtrain shape: ', Xtrain.shape)\n",
        "print('after transpose: Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "\n",
        "\"\"\" Training dataset\"\"\"\n",
        "class TrainDS(torch.utils.data.Dataset):\n",
        "    def __init__(self):\n",
        "        self.len = Xtrain.shape[0]\n",
        "        self.x_data = torch.FloatTensor(Xtrain)\n",
        "        self.y_data = torch.LongTensor(ytrain)\n",
        "    def __getitem__(self, index):\n",
        "        # 根据索引返回数据和对应的标签\n",
        "        return self.x_data[index], self.y_data[index]\n",
        "    def __len__(self):\n",
        "        # 返回文件数据的数目\n",
        "        return self.len\n",
        "\n",
        "\"\"\" Testing dataset\"\"\"\n",
        "class TestDS(torch.utils.data.Dataset):\n",
        "    def __init__(self):\n",
        "        self.len = Xtest.shape[0]\n",
        "        self.x_data = torch.FloatTensor(Xtest)\n",
        "        self.y_data = torch.LongTensor(ytest)\n",
        "    def __getitem__(self, index):\n",
        "        # 根据索引返回数据和对应的标签\n",
        "        return self.x_data[index], self.y_data[index]\n",
        "    def __len__(self):\n",
        "        # 返回文件数据的数目\n",
        "        return self.len\n",
        "\n",
        "# 创建 trainloader 和 testloader\n",
        "trainset = TrainDS()\n",
        "testset  = TestDS()\n",
        "train_loader = torch.utils.data.DataLoader(dataset=trainset, batch_size=128, shuffle=True, num_workers=2)\n",
        "test_loader  = torch.utils.data.DataLoader(dataset=testset,  batch_size=128, shuffle=False, num_workers=2)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eMGl-jdAP3p",
        "outputId": "aabff548-f773-4ab4-a7bf-e23014e6dc4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hyperspectral data shape:  (145, 145, 200)\n",
            "Label shape:  (145, 145)\n",
            "\n",
            "... ... PCA tranformation ... ...\n",
            "Data shape after PCA:  (145, 145, 30)\n",
            "\n",
            "... ... create data cubes ... ...\n",
            "Data cube X shape:  (10249, 25, 25, 30)\n",
            "Data cube y shape:  (10249,)\n",
            "\n",
            "... ... create train & test data ... ...\n",
            "Xtrain shape:  (1024, 25, 25, 30)\n",
            "Xtest  shape:  (9225, 25, 25, 30)\n",
            "before transpose: Xtrain shape:  (1024, 25, 25, 30, 1)\n",
            "before transpose: Xtest  shape:  (9225, 25, 25, 30, 1)\n",
            "after transpose: Xtrain shape:  (1024, 1, 30, 25, 25)\n",
            "after transpose: Xtest  shape:  (9225, 1, 30, 25, 25)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 配置训练环境"
      ],
      "metadata": {
        "id": "4MXbnIKpAVlM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 使用GPU训练，可以在菜单 \"代码执行工具\" -> \"更改运行时类型\" 里进行设置\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# 网络放到GPU上\n",
        "net = HybridSN().to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(net.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "_DCmlcK4AWXa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 开始训练，保存训练过程的损失下降和模型(有Dropout控制)"
      ],
      "metadata": {
        "id": "dPy3JXWCAaLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 记录每次epoch的平均训练损失\n",
        "train_losses_avg = []\n",
        "# 记录每次epoch的当前损失\n",
        "train_losses_current = []\n",
        "# 开始训练，加入dropout控制\n",
        "net.train()\n",
        "total_loss = 0\n",
        "for epoch in range(200):\n",
        "    current_loss = 0\n",
        "    for i, (inputs, labels) in enumerate(train_loader):\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # 优化器梯度归零\n",
        "        optimizer.zero_grad()\n",
        "        # 正向传播 +　反向传播 + 优化\n",
        "        outputs = net(inputs)\n",
        "\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        current_loss += loss.item()\n",
        "    train_losses_avg.append(total_loss / (epoch+1))\n",
        "    train_losses_current.append(current_loss)\n",
        "    print('[Epoch: %d]   [loss avg: %.4f]   [current loss: %.4f]' %(epoch + 1, total_loss/(epoch+1), current_loss))\n",
        "\n",
        "print('Finished Training')\n",
        "\n",
        "# 可视化训练损失\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_losses_avg, label='Average Loss per Epoch')\n",
        "plt.plot(train_losses_current, label='Current Loss per Epoch')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss per Epoch')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# 保存图像\n",
        "plt.savefig(f'training_loss_SE_NoSoftmax_DropoutControl.png')\n",
        "\n",
        "# 显示图像\n",
        "plt.show()\n",
        "\n",
        "# 保存训练后的模型\n",
        "torch.save(net.state_dict(), 'HybirdSN_SE_NoSoftmax_DropoutControl.pth')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iR8AuiT0Aayc",
        "outputId": "401915f9-2c93-45f2-c73b-12ec5122300c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch: 1]   [loss avg: 21.4710]   [current loss: 21.4710]\n",
            "[Epoch: 2]   [loss avg: 20.7709]   [current loss: 20.0709]\n",
            "[Epoch: 3]   [loss avg: 20.2446]   [current loss: 19.1918]\n",
            "[Epoch: 4]   [loss avg: 19.6830]   [current loss: 17.9982]\n",
            "[Epoch: 5]   [loss avg: 19.1175]   [current loss: 16.8558]\n",
            "[Epoch: 6]   [loss avg: 18.5714]   [current loss: 15.8406]\n",
            "[Epoch: 7]   [loss avg: 17.9466]   [current loss: 14.1977]\n",
            "[Epoch: 8]   [loss avg: 17.2322]   [current loss: 12.2315]\n",
            "[Epoch: 9]   [loss avg: 16.4984]   [current loss: 10.6282]\n",
            "[Epoch: 10]   [loss avg: 15.7656]   [current loss: 9.1702]\n",
            "[Epoch: 11]   [loss avg: 15.0508]   [current loss: 7.9030]\n",
            "[Epoch: 12]   [loss avg: 14.3089]   [current loss: 6.1477]\n",
            "[Epoch: 13]   [loss avg: 13.5929]   [current loss: 5.0009]\n",
            "[Epoch: 14]   [loss avg: 12.8811]   [current loss: 3.6276]\n",
            "[Epoch: 15]   [loss avg: 12.2110]   [current loss: 2.8302]\n",
            "[Epoch: 16]   [loss avg: 11.6132]   [current loss: 2.6466]\n",
            "[Epoch: 17]   [loss avg: 11.0338]   [current loss: 1.7630]\n",
            "[Epoch: 18]   [loss avg: 10.4981]   [current loss: 1.3903]\n",
            "[Epoch: 19]   [loss avg: 10.0063]   [current loss: 1.1549]\n",
            "[Epoch: 20]   [loss avg: 9.5546]   [current loss: 0.9717]\n",
            "[Epoch: 21]   [loss avg: 9.1295]   [current loss: 0.6277]\n",
            "[Epoch: 22]   [loss avg: 8.7414]   [current loss: 0.5922]\n",
            "[Epoch: 23]   [loss avg: 8.3808]   [current loss: 0.4475]\n",
            "[Epoch: 24]   [loss avg: 8.0516]   [current loss: 0.4781]\n",
            "[Epoch: 25]   [loss avg: 7.7543]   [current loss: 0.6193]\n",
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            "[Epoch: 186]   [loss avg: 1.1438]   [current loss: 0.1033]\n",
            "[Epoch: 187]   [loss avg: 1.1390]   [current loss: 0.2570]\n",
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            "[Epoch: 189]   [loss avg: 1.1280]   [current loss: 0.0281]\n",
            "[Epoch: 190]   [loss avg: 1.1230]   [current loss: 0.1740]\n",
            "[Epoch: 191]   [loss avg: 1.1200]   [current loss: 0.5545]\n",
            "[Epoch: 192]   [loss avg: 1.1147]   [current loss: 0.0934]\n",
            "[Epoch: 193]   [loss avg: 1.1099]   [current loss: 0.1879]\n",
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            "[Epoch: 195]   [loss avg: 1.1004]   [current loss: 0.1836]\n",
            "[Epoch: 196]   [loss avg: 1.0953]   [current loss: 0.0991]\n",
            "[Epoch: 197]   [loss avg: 1.0899]   [current loss: 0.0488]\n",
            "[Epoch: 198]   [loss avg: 1.0855]   [current loss: 0.2057]\n",
            "[Epoch: 199]   [loss avg: 1.0807]   [current loss: 0.1350]\n",
            "[Epoch: 200]   [loss avg: 1.0758]   [current loss: 0.1103]\n",
            "Finished Training\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 模型测试，并可视化准确率"
      ],
      "metadata": {
        "id": "_62ED2FMBnzt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 控制第一次迭代时是否初始化预测结果数组\n",
        "count = 0\n",
        "# 记录测试集中分类正确的样本数量\n",
        "correct_test = 0\n",
        "# 记录测试集中的总样本数量\n",
        "total_test = 0\n",
        "# 记录每个batch的准确率\n",
        "val_accuracies = []\n",
        "# 开始测试，加入dropout控制\n",
        "net.eval()\n",
        "# 在测试集上计算准确率并预测结果\n",
        "for inputs, labels in test_loader:\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    outputs = net(inputs)\n",
        "\n",
        "    # 获取预测结果\n",
        "    _, predicted = torch.max(outputs, 1)\n",
        "\n",
        "    # 更新测试准确率\n",
        "    correct_test += (predicted == labels).sum().item()\n",
        "    total_test += labels.size(0)\n",
        "\n",
        "    # 将预测结果添加到y_pred_test\n",
        "    outputs = np.argmax(outputs.detach().cpu().numpy(), axis=1)\n",
        "    if count == 0:\n",
        "        y_pred_test = outputs\n",
        "        count = 1\n",
        "    else:\n",
        "        y_pred_test = np.concatenate((y_pred_test, outputs))\n",
        "\n",
        "    # 计算当前batch的准确率\n",
        "    batch_accuracy = (predicted == labels).sum().item() / labels.size(0)\n",
        "    val_accuracies.append(batch_accuracy)\n",
        "\n",
        "# 总准确率\n",
        "test_accuracy = correct_test / total_test\n",
        "\n",
        "# 生成分类报告\n",
        "classification = classification_report(ytest, y_pred_test, digits=4)\n",
        "print(classification)\n",
        "\n",
        "# 可视化准确率\n",
        "plt.plot(val_accuracies, label='Test Accuracy per Batch')\n",
        "plt.axhline(y=test_accuracy, color='r', linestyle='--', label=f'Overall Accuracy: {test_accuracy:.4f}')\n",
        "plt.title('Test Accuracy During Testing')\n",
        "plt.xlabel('Batch Index')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# 保存图像\n",
        "plt.savefig(f'testing_accuracy_SE_NoSoftmax_DropoutControl.png')\n",
        "\n",
        "# 显示图像\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 871
        },
        "id": "jV2OsGFNBrGQ",
        "outputId": "1790e034-6c17-4aac-da90-32c55a66e999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0     0.9500    0.9268    0.9383        41\n",
            "         1.0     0.9934    0.9370    0.9644      1285\n",
            "         2.0     0.9626    0.9987    0.9803       747\n",
            "         3.0     0.9848    0.9155    0.9489       213\n",
            "         4.0     0.9885    0.9885    0.9885       435\n",
            "         5.0     0.9731    0.9909    0.9819       657\n",
            "         6.0     1.0000    1.0000    1.0000        25\n",
            "         7.0     0.9954    1.0000    0.9977       430\n",
            "         8.0     1.0000    0.8889    0.9412        18\n",
            "         9.0     0.9852    0.9920    0.9886       875\n",
            "        10.0     0.9632    0.9950    0.9789      2210\n",
            "        11.0     0.9862    0.9345    0.9596       534\n",
            "        12.0     1.0000    0.9784    0.9891       185\n",
            "        13.0     0.9965    0.9930    0.9947      1139\n",
            "        14.0     0.9688    0.9827    0.9757       347\n",
            "        15.0     0.8941    0.9048    0.8994        84\n",
            "\n",
            "    accuracy                         0.9789      9225\n",
            "   macro avg     0.9776    0.9642    0.9704      9225\n",
            "weighted avg     0.9792    0.9789    0.9787      9225\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 显示预测图像与图例"
      ],
      "metadata": {
        "id": "eYmqBFzDB0MB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load the original image\n",
        "X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']\n",
        "y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']\n",
        "\n",
        "height = y.shape[0]\n",
        "width = y.shape[1]\n",
        "\n",
        "X = applyPCA(X, numComponents= pca_components)\n",
        "X = padWithZeros(X, patch_size//2)\n",
        "\n",
        "# 逐像素预测类别\n",
        "outputs = np.zeros((height,width))\n",
        "for i in range(height):\n",
        "    for j in range(width):\n",
        "        if int(y[i,j]) == 0:\n",
        "            continue\n",
        "        else :\n",
        "            image_patch = X[i:i+patch_size, j:j+patch_size, :]\n",
        "            image_patch = image_patch.reshape(1,image_patch.shape[0],image_patch.shape[1], image_patch.shape[2], 1)\n",
        "            X_test_image = torch.FloatTensor(image_patch.transpose(0, 4, 3, 1, 2)).to(device)\n",
        "            prediction = net(X_test_image)\n",
        "            prediction = np.argmax(prediction.detach().cpu().numpy(), axis=1)\n",
        "            outputs[i][j] = prediction+1\n",
        "    # if i % 20 == 0:\n",
        "    #     print('... ... row ', i, ' handling ... ...')\n",
        "\n",
        "# 定义Indian Pines的类别标签\n",
        "target_names = ['Alfalfa', 'Corn-notill', 'Corn-mintill', 'Corn',\n",
        "                'Grass-pasture', 'Grass-trees', 'Grass-pasture-mowed',\n",
        "                'Hay-windrowed', 'Oats', 'Soybean-notill', 'Soybean-mintill',\n",
        "                'Soybean-clean', 'Wheat', 'Woods', 'Buildings-Grass-Trees-Drives',\n",
        "                'Stone-Steel-Towers']\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "import numpy as np\n",
        "from matplotlib.colors import Normalize\n",
        "\n",
        "# 设置颜色映射表和归一化\n",
        "cmap = plt.cm.Spectral\n",
        "norm = Normalize(vmin=1, vmax=len(target_names))  # 确保vmin和vmax是基于类别数来设定\n",
        "\n",
        "# 显示预测图像，使用 matplotlib 的 imshow 来绘制\n",
        "plt.imshow(outputs, cmap=cmap, norm=norm)\n",
        "plt.colorbar()  # 显示色条\n",
        "plt.title('Predicted Image')\n",
        "\n",
        "# 创建自定义图例\n",
        "patches = [mpatches.Patch(color=cmap(i / len(target_names)), label=label)\n",
        "           for i, label in enumerate(target_names,start=1)]\n",
        "\n",
        "# 显示图例\n",
        "plt.legend(handles=patches, bbox_to_anchor=(1.25, 0.95), loc='upper left', borderaxespad=0.)\n",
        "\n",
        "# 保存图像到本地文件\n",
        "plt.savefig('predicted_image_SE_NoSoftmax_DropoutControl.png', bbox_inches='tight')  # 保存为PNG文件，'tight' 会自动调整边距\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "j8X5gJqNCEfn",
        "outputId": "c64b49ae-2381-4f03-aa6d-131f62d06b1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-f5f0d08a0190>:23: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  outputs[i][j] = prediction+1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}